Nonequivalent Effects of Diverse LogP Algorithms in Three QSAR Studies

A best​ comprehension about the toxicity of phenylsulfonyl carboxylates in Vibrio fischeri using quantitative structure activity/property relationship methods

Aromatic sulfones comprise a class of chemicals used in agrochemical and pharmaceutical industries and as floatation and extractant agents in petrochemical and metallurgy industries. In this study, new QSA(P)R studies were carried out to predict the toxicity against Vibrio fischeri of a set of 52 aromatic sulfones. The same approach was used to evaluate the relationship between these endpoint and the water solubility, another important environmental endpoint. The study resulted in models of good statistical quality and mechanistic interpretation with a possible correlation between the two endpoints, but the toxic effect is also likely to depend on other physicochemical properties. The use of the PLS2, a method not commonly used in QSA(P)R studies, also produced models of greater reliability, and the relationship between the two endpoints was reinforced to some degree. These results are useful for better understanding the process by which these compounds exert their environmental toxicity, thus aiding in the development of industrially useful compounds with less potential environmental damage.

The effect of different log P algorithms on the modeling of the soil sorption coefficient of nonionic pesticides

Collecting data on the effects of pesticides on the environment is a slow and costly process. Therefore, significant efforts have been focused on the development of models that predict physical, chemical or biological properties of environmental interest. The soil sorption coefficient normalized to the organic carbon content (Koc) is a key parameter that is used in environmental risk assessments. Thus, several log Koc prediction models that use the hydrophobic parameter log P as a descriptor have been reported in the literature. Often, algorithms are used to calculate the value of log P due to the lack of experimental values for this property. Despite the availability of various algorithms, previous studies fail to describe the procedure used to select the appropriate algorithm. In this study, models that correlate log Koc with log P were developed for a heterogeneous group of nonionic pesticides using different freeware algorithms. The statistical qualities and predictive power of all of the models were evaluated. Thus, this study was conducted to assess the effect of the log P algorithm choice on log Koc modeling. The results clearly demonstrate that the lack of a selection criterion may result in inappropriate prediction models. Seven algorithms were tested, of which only two (ALOGPS and KOWWIN) produced good results. A sensible choice may result in simple models with statistical qualities and predictive power values that are comparable to those of more complex models. Therefore, the selection of the appropriate log P algorithm for modeling log Koc cannot be arbitrary but must be based on the chemical structure of compounds and the characteristics of the available algorithms.

Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks

In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.